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Machine Learning and Symptom Patterns in Degenerative Cervical Myelopathy: Web-Based Survey Study
K-means clustering was used due to its efficiency for small data sets and explainability, aiming to group respondents into clusters based on their clinical features, using the Euclidean distance measure and the Hartigan-Wong algorithm [27]. The optimal number of clusters (k) was determined through the inspection of 3 ancillary methods, namely, the elbow, silhouette, and gap statistic methods [28].
JMIR Form Res 2024;8:e54747
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